Shapley Value-based Approach for Redistributing Revenue of Matchmaking of Private Transactions in Blockchains

TL;DR

This paper introduces a fair revenue redistribution method for blockchain matchmaking using Shapley values, addressing computational challenges with a randomized approximation algorithm and analyzing its theoretical properties.

Contribution

It formalizes the matchmaking problem in MEV using cooperative game theory and proposes a practical randomized algorithm for Shapley value approximation.

Findings

The RST-Game is a characteristic form game for transaction creators.

Exact computation of Shapley value is SUBEXP, necessitating approximation.

The proposed randomized algorithm effectively approximates the Shapley value in experiments.

Abstract

In the context of blockchain, MEV refers to the maximum value that can be extracted from block production through the inclusion, exclusion, or reordering of transactions. Searchers often participate in order flow auctions (OFAs) to obtain exclusive rights to private transactions, available through entities called matchmakers, also known as order flow providers (OFPs). Most often, redistributing the revenue generated through such auctions among transaction creators is desirable. In this work, we formally introduce the matchmaking problem in MEV, its desirable properties, and associated challenges. Using cooperative game theory, we formalize the notion of fair revenue redistribution in matchmaking and present its potential possibilities and impossibilities. Precisely, we define a characteristic form game, referred to as RST-Game, for the transaction creators. We propose to redistribute the revenue using the Shapley value of RST-Game. We show that the corresponding problem could be SUBEXP (i.e. $2^{o(n)}$, where $n$ is the number of transactions); therefore, approximating the Shapley value is necessary. Further, we propose a randomized algorithm for computing the Shapley value in RST-Game and empirically verify its efficacy.